4e55871112
* initial telemetry manager impl
* adding todo
* updates
* updates
* progress on telemetry: config load, process, model load, train start / end, error tracking
* update error file path sanitization function; adding more error tracking
* updated sanitization logic, tests
* adding runtime metrics (cpu + gpu memory, steps/s, etc.)
* tests for runtime metrics telemetry and assoc. callback
* small update / fix
* simplifying path redaction
* sleep on all ranks in distributed setting
* adding back in base_model redaction w/ whitelist
* fix
* doc update
* improved redaction, send system info during model config load telemetry, etc.
* adding runtime metrics / system info additional accelerator support, etc.
* adding runtime metrics / system info additional accelerator support, etc.
* remove duplicate info
* fixes
* fix issue with tests in ci
* distributed fix
* opt-in version of telemetry
* enable / disable logic update
* docs fix
* doc update
* minor fixes
* simplifying
* slight changes
* fix
* lint
* update posthog dep
* coderabbit comments
* fix: opt-in model
* fix: increase time since last
* fix: increase whitelist orgs
* fix: posthog init and shutdown
* fix: imports
* fix: also check grad norm
* fix: duplicate plugin_manager calls
* fix: bad merge
* chore: update docs
* fix: cache process per comment
* fix: error handling
* fix: tests
* Revert "fix: error handling"
This reverts commit 22d1ea5755.
* fix: test telemetry error_handled bool
* fix: revert test
* chore: final doc fixes
---------
Co-authored-by: Dan Saunders <danjsaund@gmail.com>
Co-authored-by: Dan Saunders <dan@axolotl.ai>
62 lines
2.4 KiB
Plaintext
62 lines
2.4 KiB
Plaintext
---
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title: Telemetry
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description: A description of the telemetry implementation in Axolotl.
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---
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# Telemetry in Axolotl
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Axolotl implements anonymous telemetry to help maintainers understand how the library
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is used and where users encounter issues. This data helps prioritize features, optimize
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performance, and fix bugs.
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## Data Collection
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We collect:
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- System info: OS, Python version, Axolotl version, PyTorch version, Transformers
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version, etc.
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- Hardware info: CPU count, memory, GPU count and models
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- Runtime metrics: Training progress, memory usage, timing information
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- Usage patterns: Models (from a whitelist) and configurations used
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- Error tracking: Stack traces and error messages (sanitized to remove personal
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information)
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Personally identifiable information (PII) is not collected.
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## Implementation
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Telemetry is implemented using PostHog and consists of:
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- `axolotl.telemetry.TelemetryManager`: A singleton class that initializes the
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telemetry system and provides methods for tracking events.
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- `axolotl.telemetry.errors.send_errors`: A decorator that captures exceptions and
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sends sanitized stack traces.
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- `axolotl.telemetry.runtime_metrics.RuntimeMetricsTracker`: A class that tracks
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runtime metrics during training.
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- `axolotl.telemetry.callbacks.TelemetryCallback`: A Trainer callback that sends
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runtime metrics telemetry.
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The telemetry system will block training startup for 10 seconds to ensure users are
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aware of data collection, unless telemetry is explicitly enabled or disabled.
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## Opt-Out Mechanism
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Telemetry is **enabled by default** on an opt-out basis. To disable it, set
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`AXOLOTL_DO_NOT_TRACK=1` or `DO_NOT_TRACK=1`.
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A warning message will be logged on start to clearly inform users about telemetry.
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We will remove this after some period.
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To hide the warning message about telemetry that is displayed on train, etc. startup,
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explicitly set: `AXOLOTL_DO_NOT_TRACK=0` (enable telemetry) or `AXOLOTL_DO_NOT_TRACK=1`
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(explicitly disable telemetry).
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## Privacy
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- All path-like config information is automatically redacted from telemetry data
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- Model information is only collected for whitelisted organizations
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- See `axolotl/telemetry/whitelist.yaml` for the set of whitelisted organizations
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- Each run generates a unique anonymous ID
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- This allows us to link different telemetry events in a single same training run
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- Telemetry is only sent from the main process to avoid duplicate events
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